The error rate desired.
Internal Partition Bloom Filters
The initial size of this filter in number of elements, not in bytes.
The load factor of each filter, By default: 0.5 half of the set
Static value, will power the size of the new set, by default we will follow a power of 2.
Add a new element to the filter
Return the current capacity (number of elements) of this filter
Create a Scalable Bloom Filter based on Partitionned Bloom Filter.
the starting size of the filter
ther error rate desired of the filter
the load factor desired
Check if two ScalableBloomFilter are equal
Load an Object from a provided JSON object
the JSON object to load
Return the Object loaded from the provided JSON object
Return True if the element has been found, false otherwise. Check until we found the value in a filter otherwise stop on the first value found.
Return a next random seeded int32 integer
Return the current false positive rate of this structure
Save the current structure as a JSON
Get a function used to draw random number
A factory function used to draw random integer
the new seed that will be used in this structure
Generated using TypeDoc
A Scalable Bloom Filter is a variant of Bloom Filters that can adapt dynamically to the number of elements stored, while assuring a maximum false positive probability
Reference: ALMEIDA, Paulo Sérgio, BAQUERO, Carlos, PREGUIÇA, Nuno, et al. Scalable bloom filters. Information Processing Letters, 2007, vol. 101, no 6, p. 255-261.
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.725.390&rep=rep1&type=pdf
Thomas Minier & Arnaud Grall